To the best of our knowledge, HateXplain [1]} is the first hate speech detection benchmark dataset that considers both these aspects. They proposed a method that utilizes rationales as attention ground truths to complement the performance of the two elements.
However, when most tokens are annotated as the human rationale in a hateful sentence, the rationale's information could be meaningless as the ground truth attention becomes hard to be distinguished from that of a normal sentence, as shown in Figure REF . This can hinder the model's learning.
